PorterG2003

I recently bought a Samsung 970 EVO 500GB NAND M.2 SDD and it isn't detected by windows, linux, or the bios. In the bios under sata ports there is a M.2 drive; it says it is not detected, yet when I go to create a new raid volume it shows there yet is not accesable (I am not wanting to create a new raid volume with it; I just was exploring the bios to see if it was detected anywhere). I am using the NZXT N7 motherboard.

have you tried support

import numpy as np from scipy import optimize class Neural_Network(object): def __init__(self, Lambda=0): #Define HyperParameters self.inputLayerSize = 2 self.outputLayerSize = 1 self.hiddenLayerSize = 3 #Weights (Parameters) self.W1 = np.random.randn(self.inputLayerSize, self.hiddenLayerSize) self.W2 = np.random.randn(self.hiddenLayerSize, self.outputLayerSize) #Regularization Parameter: self.Lambda = Lambda def forward(self, X): #Propagate inputs through network self.z2 = np.dot(X, self.W1) self.a2 = self.sigmoid(self.z2) self.z3 = np.dot(self.a2, self.W2) yHat = self.sigmoid(self.z3) return yHat def sigmoid(self, z): #Apply sigmoid activation function return 1/(1+np.exp(-z)) def sigmoidPrime(self, z): #Derivative of Sigmoid function return np.exp(-z)/((1+np.exp(-z))**2) def costFunction(self, X, y): #Compute cost for given X,y, use weights already stored in class. self.yHat = self.forward(X) J = 0.5*sum((y-self.yHat)**2)/X.shape[0] + (self.Lambda/2) * (sum(self.W1**2)+sum(self.W2**2)) return J def costFunctionPrime(self, X, y): #Compute derivative with respect to W1 and W2 self.yHat = self.forward(X) delta3 = np.multiply(-(y-self.yHat), self.sigmoidPrime(self.z3)) dJdW2 = np.dot(self.a2.T, delta3)/X.shape[0] + self.Lambda*self.W2 delta2 = np.dot(delta3, self.W2.T)*self.sigmoidPrime(self.z2) dJdW1 = np.dot(X.T, delta2)/X.shape[0] + self.Lambda*self.W1 return dJdW1, dJdW2 def getParams(self): #Get W1 andW3 Rolled into vector: params = np.concatenate((self.W1.ravel(), self.W2.ravel())) return params def setParams(self, params): #Set W1 and W2 using single parameter vector: W1_start = 0 W1_end = self.hiddenLayerSize*self.inputLayerSize self.W1 = np.reshape(params[W1_start:W1_end], (self.inputLayerSize, self.hiddenLayerSize)) W2_end = W1_end + self.hiddenLayerSize*self.outputLayerSize self.W2 = np.reshape(params[W1_end:W2_end], (self.hiddenLayerSize, self.outputLayerSize)) def computeGradients(self, X, y): dJdW1, dJdW2 = self.costFunctionPrime(X, y) return np.concatenate((dJdW1.ravel(), dJdW2.ravel())) def computeNumericalGradient(N, X, y): paramsInitial = N.getParams() numgrad = np.zeros(paramsInitial.shape) perturb = np.zeros(paramsInitial.shape) e = 1e-4 for p in range(len(paramsInitial)): #Set perturbation vector perturb[p] = e N.setParams(paramsInitial + perturb) loss2 = N.costFunction(X, y) N.setParams(paramsInitial - perturb) loss1 = N.costFunction(X, y) #Compute Nemerical Gradient: numgrad[p] = (loss2 - loss1) / (2*e) #Return the value we changed back to zero: perturb[p] = 0 #Return Params to original value: N.setParams(paramsInitial) return numgrad class trainer(object): def __init__(self, N): #Make Local reference to network: self.N = N def callbackF(self, params): self.N.setParams(params) self.J.append(self.N.costFunction(self.X, self.y)) self.testJ.append(self.N.costFunction(self.testX, self.testY)) def costFunctionWrapper(self, params, X, y): self.N.setParams(params) cost = self.N.costFunction(X, y) grad = self.N.computeGradients(X,y) return cost, grad def train(self, trainX, trainY, testX, testY): #Make an internal variable for the callback function: self.X = trainX self.y = trainY self.testX = testX self.testY = testY #Make empty list to store training costs: self.J = [] self.testJ = [] params0 = self.N.getParams() options = {'maxiter': 200, 'disp' : True} _res = optimize.minimize(self.costFunctionWrapper, params0, jac=True, method='BFGS', args=(trainX, trainY), options=options, callback=self.callbackF) self.N.setParams(_res.x) self.optimizationResults = _res def main(): # X = (hours sleeping, hours studying), y = Score on test trainX = np.array(([3,5], [5,1], [10,2], [6,1.5]), dtype=float) trainY = np.array(([75], [82], [93], [70]), dtype=float) testX = np.array(([4, 5.5], [4.5, 1], [9, 2.5], [6, 2]), dtype=float) testY = np.array(([70], [89], [85], [75]), dtype=float) trainX = trainX/np.amax(trainX, axis=0) trainY = trainY/100 #Max test score is 100 testX = testX/np.amax(trainX, axis=0) testY = testY/100 #Max test score is 100 NN = Neural_Network(Lambda=0.0001) cost1 = NN.costFunction(trainX,trainY) dJdW1, dJdW2 = NN.costFunctionPrime(trainX, trainY) scalar = 3 NN.W1 = NN.W1 + scalar*dJdW1 NN.W2 = NN.W2 + scalar*dJdW2 cost2 = NN.costFunction(trainX,trainY) dJdW1, dJdW2 = NN.costFunctionPrime(trainX, trainY) NN.W1 = NN.W1 - scalar*dJdW1 NN.W2 = NN.W2 - scalar*dJdW2 cost3 = NN.costFunction(trainX, trainY) #numgrad = NN.computeNumericalGradient(trainX, trainY) grad = NN.computeGradients(trainX, trainY) #print(np.linalg.norm(grad-numgrad)/np.linalg.norm(grad+numgrad)) T = trainer(NN) T.train(trainX, trainY, testX, testY) main() I went through the neural network playlist on youtube from welch labs i finished it but when I went through the last video my code broke. There is something wrong with how I did optimize.minimize from scipy. the error is in the picture.

Yeah getting the 75hz monitor is a good choice. Unless you are doing competitive gaming the response time shouldn't matter that much. Anything under 5 ms should be fine

ok thank you!

The higher refresh rate will do nothing if it your console runs at 60 fps and the monitor is 75hz. In my opinion the response time is more important in this case. If you are thinking of upgrading to pc later then you might consider getting a higher hz monitor to use for your xbox now and later use for your pc.

I have 16GB of ram right now. I have 2 8GB sticks. I want to get 2 more sticks for my 4 slot motherboard. Would it be ok to get two 16GB sticks to have a total of 48 GB of ram?

that gave me an error for the colon after the if statement then one for syntax of sys.exit(main())

def mergeSort(lst): lst = [[i] for i in lst] while len(lst) > 1: for i in lst: c = 0 if lst.index(i) == len(lst)-1: k = lst[lst.index(i)-1] else: k = lst[lst.index(i)+1] while len(k) > 0: if c == len(i): i.append(k.pop(0)) elif i[c] > k[0]: i.insert(c, k.pop(0)) c += 1 else: c += 1 lst.remove(k) return lst def main(): print() print(mergeSort([random.randint(-1000,1000) for i in range(10000)]) main() Anyway to fix this?

I would get a gtx 1060 if I were you

I bought another monitor and it needed an hdmi port. I went into my BIOS then turned on integrated graphics on my chipset (so I could use the port on the motherboard and the port on the graphics card). It works and duplicates the screen in the BIOS but when I boot up windows it just goes black. I try going into the display settings and says no other displays are detected. How can I fix this?

ok yeah i think that i will get this monitor

ok thank you it is ips but what is the difference of ips and tn?

ok do you know of any other monitors that might be better?

I'm looking for a good gaming monitor for not a lot of money. I came upon the hp 23er. Although it is not advertised for gaming is the 7ms good?